Proceedings of the twenty-first international conference on Machine learning最新文献

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SVM-based generalized multiple-instance learning via approximate box counting 基于svm的近似盒计数广义多实例学习
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015405
Qingping Tao, S. Scott, N. V. Vinodchandran, T. T. Osugi
{"title":"SVM-based generalized multiple-instance learning via approximate box counting","authors":"Qingping Tao, S. Scott, N. V. Vinodchandran, T. T. Osugi","doi":"10.1145/1015330.1015405","DOIUrl":"https://doi.org/10.1145/1015330.1015405","url":null,"abstract":"The multiple-instance learning (MIL) model has been very successful in application areas such as drug discovery and content-based image-retrieval. Recently, a generalization of this model and an algorithm for this generalization were introduced, showing significant advantages over the conventional MIL model in certain application areas. Unfortunately, this algorithm is inherently inefficient, preventing scaling to high dimensions. We reformulate this algorithm using a kernel for a support vector machine, reducing its time complexity from exponential to polynomial. Computing the kernel is equivalent to counting the number of axis-parallel boxes in a discrete, bounded space that contain at least one point from each of two multisets P and Q. We show that this problem is #P-complete, but then give a fully polynomial randomized approximation scheme (FPRAS) for it. Finally, we empirically evaluate our kernel.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127546624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 100
Decision trees with minimal costs 成本最小的决策树
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015369
C. Ling, Qiang Yang, Jianning Wang, Shichao Zhang
{"title":"Decision trees with minimal costs","authors":"C. Ling, Qiang Yang, Jianning Wang, Shichao Zhang","doi":"10.1145/1015330.1015369","DOIUrl":"https://doi.org/10.1145/1015330.1015369","url":null,"abstract":"We propose a simple, novel and yet effective method for building and testing decision trees that minimizes the sum of the misclassification and test costs. More specifically, we first put forward an original and simple splitting criterion for attribute selection in tree building. Our tree-building algorithm has many desirable properties for a cost-sensitive learning system that must account for both types of costs. Then, assuming that the test cases may have a large number of missing values, we design several intelligent test strategies that can suggest ways of obtaining the missing values at a cost in order to minimize the total cost. We experimentally compare these strategies and C4.5, and demonstrate that our new algorithms significantly outperform C4.5 and its variations. In addition, our algorithm's complexity is similar to that of C4.5, and is much lower than that of previous work. Our work is useful for many diagnostic tasks which must factor in the misclassification and test costs for obtaining missing information.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126660151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 306
P3VI: a partitioned, prioritized, parallel value iterator P3VI:分区、优先级、并行值迭代器
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015440
D. Wingate, Kevin Seppi
{"title":"P3VI: a partitioned, prioritized, parallel value iterator","authors":"D. Wingate, Kevin Seppi","doi":"10.1145/1015330.1015440","DOIUrl":"https://doi.org/10.1145/1015330.1015440","url":null,"abstract":"We present an examination of the state-of-the-art for using value iteration to solve large-scale discrete Markov Decision Processes. We introduce an architecture which combines three independent performance enhancements (the intelligent prioritization of computation, state partitioning, and massively parallel processing) into a single algorithm. We show that each idea improves performance in a different way, meaning that algorithm designers do not have to trade one improvement for another. We give special attention to parallelization issues, discussing how to efficiently partition states, distribute partitions to processors, minimize message passing and ensure high scalability. We present experimental results which demonstrate that this approach solves large problems in reasonable time.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114025503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 24
Learning to learn with the informative vector machine 学习用信息向量机学习
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015382
Neil D. Lawrence, John C. Platt
{"title":"Learning to learn with the informative vector machine","authors":"Neil D. Lawrence, John C. Platt","doi":"10.1145/1015330.1015382","DOIUrl":"https://doi.org/10.1145/1015330.1015382","url":null,"abstract":"This paper describes an efficient method for learning the parameters of a Gaussian process (GP). The parameters are learned from multiple tasks which are assumed to have been drawn independently from the same GP prior. An efficient algorithm is obtained by extending the informative vector machine (IVM) algorithm to handle the multi-task learning case. The multi-task IVM (MTIVM) saves computation by greedily selecting the most informative examples from the separate tasks. The MT-IVM is also shown to be more efficient than random sub-sampling on an artificial data-set and more effective than the traditional IVM in a speaker dependent phoneme recognition task.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"45 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114123598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 375
Testing the significance of attribute interactions 测试属性交互的重要性
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015377
Aleks Jakulin, I. Bratko
{"title":"Testing the significance of attribute interactions","authors":"Aleks Jakulin, I. Bratko","doi":"10.1145/1015330.1015377","DOIUrl":"https://doi.org/10.1145/1015330.1015377","url":null,"abstract":"Attribute interactions are the irreducible dependencies between attributes. Interactions underlie feature relevance and selection, the structure of joint probability and classification models: if and only if the attributes interact, they should be connected. While the issue of 2-way interactions, especially of those between an attribute and the label, has already been addressed, we introduce an operational definition of a generalized n-way interaction by highlighting two models: the reductionistic part-to-whole approximation, where the model of the whole is reconstructed from models of the parts, and the holistic reference model, where the whole is modelled directly. An interaction is deemed significant if these two models are significantly different. In this paper, we propose the Kirkwood superposition approximation for constructing part-to-whole approximations. To model data, we do not assume a particular structure of interactions, but instead construct the model by testing for the presence of interactions. The resulting map of significant interactions is a graphical model learned from the data. We confirm that the P-values computed with the assumption of the asymptotic X2 distribution closely match those obtained with the boot-strap.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"965 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123070394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 150
Decentralized detection and classification using kernel methods 使用核方法的分散检测和分类
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015438
X. Nguyen, M. Wainwright, Michael I. Jordan
{"title":"Decentralized detection and classification using kernel methods","authors":"X. Nguyen, M. Wainwright, Michael I. Jordan","doi":"10.1145/1015330.1015438","DOIUrl":"https://doi.org/10.1145/1015330.1015438","url":null,"abstract":"We consider the problem of decentralized detection under constraints on the number of bits that can be transmitted by each sensor. In contrast to most previous work, in which the joint distribution of sensor observations is assumed to be known, we address the problem when only a set of empirical samples is available. We propose a novel algorithm using the framework of empirical risk minimization and marginalized kernels, and analyze its computational and statistical properties both theoretically and empirically. We provide an efficient implementation of the algorithm, and demonstrate its performance on both simulated and real data sets.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122802287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 17
C4.5 competence map: a phase transition-inspired approach C4.5能力图:一种受阶段转变启发的方法
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015398
Nicolas Baskiotis, M. Sebag
{"title":"C4.5 competence map: a phase transition-inspired approach","authors":"Nicolas Baskiotis, M. Sebag","doi":"10.1145/1015330.1015398","DOIUrl":"https://doi.org/10.1145/1015330.1015398","url":null,"abstract":"How to determine a priori whether a learning algorithm is suited to a learning problem instance is a major scientific and technological challenge. A first step toward this goal, inspired by the Phase Transition (PT) paradigm developed in the Constraint Satisfaction domain, is presented in this paper.Based on the PT paradigm, extensive and principled experiments allow for constructing the Competence Map associated to a learning algorithm, describing the regions where this algorithm on average fails or succeeds. The approach is illustrated on the long and widely used C4.5 algorithm. A non trivial failure region in the landscape of k-term DNF languages is observed and some interpretations are offered for the experimental results.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129146331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Feature selection, L1 vs. L2 regularization, and rotational invariance 特征选择,L1与L2正则化和旋转不变性
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015435
A. Ng
{"title":"Feature selection, L1 vs. L2 regularization, and rotational invariance","authors":"A. Ng","doi":"10.1145/1015330.1015435","DOIUrl":"https://doi.org/10.1145/1015330.1015435","url":null,"abstract":"We consider supervised learning in the presence of very many irrelevant features, and study two different regularization methods for preventing overfitting. Focusing on logistic regression, we show that using L1 regularization of the parameters, the sample complexity (i.e., the number of training examples required to learn \"well,\") grows only logarithmically in the number of irrelevant features. This logarithmic rate matches the best known bounds for feature selection, and indicates that L1 regularized logistic regression can be effective even if there are exponentially many irrelevant features as there are training examples. We also give a lower-bound showing that any rotationally invariant algorithm---including logistic regression with L2 regularization, SVMs, and neural networks trained by backpropagation---has a worst case sample complexity that grows at least linearly in the number of irrelevant features.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117205970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1809
Utile distinction hidden Markov models 无差别隐马尔可夫模型
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015346
Daan Wierstra, M. Wiering
{"title":"Utile distinction hidden Markov models","authors":"Daan Wierstra, M. Wiering","doi":"10.1145/1015330.1015346","DOIUrl":"https://doi.org/10.1145/1015330.1015346","url":null,"abstract":"This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Partially Observable Markov Decision Processes. We present a novel approach that uses a modification of the well-known Baum-Welch algorithm for learning a Hidden Markov Model (HMM) to predict both percepts and utility in a non-deterministic world. This enables an agent to make decisions based on its previous history of actions, observations, and rewards. Our algorithm, called Utile Distinction Hidden Markov Models (UDHMM), handles the creation of memory well in that it tends to create perceptual and utility distinctions only when needed, while it can still discriminate states based on histories of arbitrary length. The experimental results in highly stochastic problem domains show very good performance.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125876658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 24
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5 具有许多冗余特征的文本分类:使用积极的特征选择使svm与C4.5竞争
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015388
E. Gabrilovich, Shaul Markovitch
{"title":"Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5","authors":"E. Gabrilovich, Shaul Markovitch","doi":"10.1145/1015330.1015388","DOIUrl":"https://doi.org/10.1145/1015330.1015388","url":null,"abstract":"Text categorization algorithms usually represent documents as bags of words and consequently have to deal with huge numbers of features. Most previous studies found that the majority of these features are relevant for classification, and that the performance of text categorization with support vector machines peaks when no feature selection is performed. We describe a class of text categorization problems that are characterized with many redundant features. Even though most of these features are relevant, the underlying concepts can be concisely captured using only a few features, while keeping all of them has substantially detrimental effect on categorization accuracy. We develop a novel measure that captures feature redundancy, and use it to analyze a large collection of datasets. We show that for problems plagued with numerous redundant features the performance of C4.5 is significantly superior to that of SVM, while aggressive feature selection allows SVM to beat C4.5 by a narrow margin.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126578974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 246
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